TY - CHAP AU - Qin, Yali AU - Xie, Shiwen AU - Xie, Yongfang TI - Evaluation of Zinc Roughing Process Based on Weighted Density Clustering T2 - 2025 37th Chinese Control and Decision Conference (CCDC) PB - IEEE CY - Piscataway (NJ) SN - 9798331510565 PY - 2025 SP - 1592 EP - 1597 PG - 6 DO - 10.1109/CCDC65474.2025.11090896 UR - https://m2.mtmt.hu/api/publication/36313532 ID - 36313532 LA - English DB - MTMT ER - TY - JOUR AU - Azzouzi, Souad AU - Hjouji, Amal AU - EL-Mekkaoui, Jaouad AU - EL Khalfi, Ahmed TI - An improved image clustering algorithm based on Kernel method and Tchebychev orthogonal moments JF - EVOLUTIONARY INTELLIGENCE J2 - EVOL INTELL VL - 16 PY - 2023 SP - 1237 EP - 1258 PG - 22 SN - 1864-5909 DO - 10.1007/s12065-022-00734-x UR - https://m2.mtmt.hu/api/publication/33447095 ID - 33447095 AB - In this paper, we introduce a new clustering algorithm called Improved Kernel Possibilistic Fuzzy C-Means algorithm (ImKPFCM), based on the kernel method and possibilistic approach. The proposed ImKPFCM algorithm corrects several FCM, PFCM and GPFCM algorithms shortcomings, reliably detects clustering centers and allows in addition to use Euclidean distance, the employment of other more powerful additional norms able to handle various complex situations. In this study, we applied ImKPFCM algorithm as a new image clustering method on the basis of Tchebychev orthogonal moments to extract feature vectors and then compared it with FCM, PFCM and GPFCM algorithms to evaluate its performance. The comparative study results applied to several image dataset, revealed that the ImKPFCM clustering algorithm improves the clustering accuracy over the FCM, PFCM and GPFCM methods. Therefore, we conclude that the ImKPFCM algorithm is more efficient and produces satisfactory image clustering results. LA - English DB - MTMT ER - TY - CHAP AU - Naghi, Mirtill-Boglárka AU - Kovács, Levente AU - Szilágyi, László TI - A review on advanced c-means clustering models based on fuzzy logic T2 - IEEE 21st World Symposium on Applied Machine Intelligence and Informatics SAMI (2023) : Proceedings PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Herlany SN - 9798350319859 PY - 2023 SP - 293 EP - 298 PG - 6 DO - 10.1109/SAMI58000.2023.10044530 UR - https://m2.mtmt.hu/api/publication/33589051 ID - 33589051 N1 - Doctoral School of Applied Mathematics and Applied Informatics, O´buda University, Budapest, Hungary Comput. Intell. Research Group, Sapientia Univ., Tg. Mures, Romania Physiological Controls Research Center, O´buda University, Budapest, Hungary Computational Intelligence Research Group Sapientia University, Tg. Mures, Romania Export Date: 13 March 2025; Cited By: 5; Correspondence Address: M.-B. Naghi; Doctoral School of Applied Mathematics and Applied Informatics, O´buda University, Budapest, Hungary; email: naghi.mirtill@ms.sapientia.ro; Conference name: 21st IEEE World Symposium on Applied Machine Intelligence and Informatics, SAMI 2023; Conference date: 19 January 2023 through 21 January 2023; Conference code: 186792 LA - English DB - MTMT ER - TY - JOUR AU - Mahfouz, Mohamed A. TI - SPCM: Efficient semi-possibilistic c-means clustering algorithm JF - JOURNAL OF INTELLIGENT & FUZZY SYSTEMS J2 - J INTELL FUZZY SYST VL - 43 PY - 2022 IS - 6 SP - 7227 EP - 7241 PG - 15 SN - 1064-1246 DO - 10.3233/JIFS-213172 UR - https://m2.mtmt.hu/api/publication/33447094 ID - 33447094 AB - The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters' centers are determined, then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms. LA - English DB - MTMT ER -